CN106872977A - A kind of chromatography SAR three-D imaging methods based on the weak orthogonal matching pursuit of segmentation - Google Patents
A kind of chromatography SAR three-D imaging methods based on the weak orthogonal matching pursuit of segmentation Download PDFInfo
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- CN106872977A CN106872977A CN201611239581.XA CN201611239581A CN106872977A CN 106872977 A CN106872977 A CN 106872977A CN 201611239581 A CN201611239581 A CN 201611239581A CN 106872977 A CN106872977 A CN 106872977A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
- G01S13/88—Radar or analogous systems specially adapted for specific applications
- G01S13/89—Radar or analogous systems specially adapted for specific applications for mapping or imaging
- G01S13/90—Radar or analogous systems specially adapted for specific applications for mapping or imaging using synthetic aperture techniques, e.g. synthetic aperture radar [SAR] techniques
Abstract
The present invention provides a kind of based on the chromatography SAR three-D imaging methods for being segmented weak orthogonal matching pursuit, comprises the following steps：The original SAR data of S1, collection target of repeatedly navigating, and the pretreatment of two-dimentional high-resolution imaging, registration and phase compensation is carried out to initial data, so as to the observation data set for obtaining to carry out chromatographing SAR imagings；S2, aperture synthetic is carried out oblique distance is vertical using many scape two-dimensional SAR data of pretreatment, and reconstruct the vertical information of oblique distance；S3, acquisition target elevation information, the three-dimensional point cloud result of display target building.The method can reconstruct the vertical information of oblique distance well in the case of the vertical signal degree of rarefication of unknown oblique distance, realize the three-dimensional imaging of target structures thing.
Description
Technical field
The present invention relates to synthetic aperture radar (Synthetic Aperture Radar, SAR) technical field of imaging, more
Body ground, is related to a kind of based on the chromatography SAR three-D imaging methods for being segmented weak orthogonal matching pursuit.
Background technology
SAR is a kind of active earth observation technology, and compared with traditional optical pickocff, SAR can realize whole day
When, round-the-clock real-time monitored over the ground, and with certain ground penetrating ability.The development of SAR technologies brings data volume and meter
The drastically expansion of calculation amount, and with the enhancing of real time imagery demand, the Real-time processing to extensive SAR echo datas is got over
Send out important.
Compressive sensing theory quickly develops in the application of chromatography SAR imaging fields.It is various to be managed based on compressed sensing
The mode of chromatography SAR multiplanar imagings has been expanded by the proposition of algorithm.What on December 26th, 2012 published《Electronic surveying and instrument
Report》" the SAR tomographies based on regularization orthogonal matching pursuit " technology is disclosed, it proposes to be chased after based on the orthogonal matching of regularization
The chromatography SAR three-dimensional imagings of track (Regularized Orthogonal Matching Pursuit, ROMP).Utilized in document
Emulate accuracy of the data verification orthogonal matching pursuit in chromatography SAR three-dimensional imagings, the chromatography SAR three-dimensional imagings based on ROMP
Vertical (in document the be referred to as height to) signal of algorithm energy Accurate Reconstruction oblique distance, realizes high-resolution imaging.Although the algorithm ensures
The robustness of reconstruct and the efficiency of computing, but ROMP is the restructing algorithm of non-self-adapting, in actual applications using ROMP
Needed when chromatographing SAR three-dimensional imagings the degree of rarefication K of known signal can accurately reconstruct the vertical letter of oblique distance of target
Breath, truly reduces the true three-dimension scene of Target scalar.And the accurate determination vertical signal of oblique distance is difficult in actual applications
Degree of rarefication, this considerably increases the difficulty of production application.
The content of the invention
The present invention provide it is a kind of overcome above mentioned problem or solve the above problems at least in part based on being segmented weak positive friendship
The chromatography SAR three-D imaging methods of match tracing, self adaptation is carried out in the case of unknown degree of rarefication well, in chromatography SAR
Reconstruct oblique distance is vertical well in three-dimensional imaging, realizes the three-dimensional imaging of target.
According to an aspect of the present invention, there is provided one kind chromatography SAR three-D imaging methods, comprise the following steps：
S1, the two-dimensional SAR image for obtaining target structures thing are simultaneously pre-processed, and constitute observation data set；
S2, the compressed sensing algorithm based on the weak orthogonal matching pursuit of segmentation reconstruct the vertical information of oblique distance；
S3, acquisition target elevation information, the three-dimensional point cloud result of display target building.
Used as preferred, the step S1 is specifically included：The multiple original SAR data of target of looking over so as to check of navigating of collection, and to original
Data carry out the pretreatment of two-dimentional high-resolution imaging, registration and phase compensation, so as to obtain can carrying out chromatographing SAR imagings
Observation data set
Used as preferred, the two-dimensional SAR image data includes that orbit altitude, revisiting period, ranges of incidence angles, center are oblique
Away from, azimuth resolution, range resolution ratio, single channel scene size, maximum obtain length.
Used as preferred, the step S2 is specifically included：
S21, the two-dimensional SAR image data of multiple scenes is carried out into aperture synthetic along oblique distance is vertical, will a wherein width as
Main image, other images carry out registration with this image, after registration in main image each pixel along the vertical same scattering list of oblique distance
Unit constitutes an observation signal；
S22, each observation signal is reconstructed using the method for being segmented weak orthogonal matching pursuit, obtains target structures thing
Along the vertical information of oblique distance.
Used as preferred, the step S21 is further included：Determination respectively is intercepted the orientation and distance of target image
To beginning and end, the signal of scattering unit is constituted into an information for time series.
Used as preferred, the step S22 is specifically included：
S221, acquisition m flight path image expression formulas Sm；
S222, by SmExpression formula carries out discrete conversion, obtain observation vector for compressed sensing algorithm, calculation matrix and
Scattering sparse matrix；
S223, carry out the vertical signal reconstruct of oblique distance.
Used as preferred, the step S221 is specifically included, the complex values S of any one pixel in main imagem, as
In the projection of x-r planes, i.e., reflected signal is along the vertical integration of oblique distance for true three-dimension reflection case：
In formula, γ (s) is to scatter volume reflectivity along the vertical distribution function of oblique distance, and λ is wavelength, b⊥mIt is flight path m, m-1
Parallax range, ro is main image center oblique distance.
Used as preferred, the step S222 is specifically included：
To m flight path image expression formulas SmCarry out discrete representation：
In formula, SnIt is n-th target, N is along the vertical scattering object number of oblique distance, m=1...M, M<<N；Above formula is passed through
Matrix is represented：
Y=Φ γ+ξ
In formula, y=Sm, it is M dimension observed quantities, represent the observed quantity of M bar flight paths；
ξ=[ξ1,ξ2,…,ξM]T, it is the white Gaussian noise being independently distributed；
Φ=[Φ1,Φ2,…Φn,…ΦN], it is the calculation matrix of M × N；
γ=[γ1,γ2…,γn,…,γN]TIt is sparse matrix；
Observation vector y, calculation matrix Φ, sparse matrix γ are carried out to be segmented weak orthogonal matching pursuit algorithm treatment, is obtained
Oblique distance information after reconstruct
Compared with prior art, the beneficial effects of the present invention are：The present invention is matched by weak positive friendship of segmentation of self adaptation
Follow the trail of (Stagewise Weak Orthogonal Matching Pursuit, SWOMP) chromatography SAR three-dimensional imaging algorithms, energy
It is enough to reconstruct the vertical information of oblique distance well in the case of the vertical signal degree of rarefication of unknown oblique distance, realize the three-dimensional of target structures thing
Imaging.This greatly expands the areas imaging of chromatography SAR three-dimensional imagings.
Brief description of the drawings
Fig. 1 is the embodiment of the present invention 1 based on the chromatography SAR three-D imaging method flow chart elements for being segmented weak orthogonal matching pursuit
Figure；
Fig. 2 is the idiographic flow schematic diagram of embodiment of the present invention Fig. 1；
Fig. 3 is the weak orthogonal matching pursuit algorithm flow chart of segmentation of the embodiment of the present invention 1；
Fig. 4 is the SAR imaging results schematic diagrames of the embodiment of the present invention 2.
Specific embodiment
With reference to the accompanying drawings and examples, specific embodiment of the invention is described in further detail.Hereinafter implement
Example is not limited to the scope of the present invention for illustrating the present invention.
Finally, the present processes are only preferably embodiment, are not intended to limit the scope of the present invention.It is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvements made etc. should be included in protection of the invention
Within the scope of.
Embodiment 1
Fig. 1 shows a kind of FB(flow block) based on the chromatography SAR three-D imaging methods for being segmented weak orthogonal matching pursuit, bag
Include following steps：
S1, the two-dimensional SAR image for obtaining target structures thing are simultaneously pre-processed, and constitute observation data set；
S2, the compressed sensing algorithm based on the weak orthogonal matching pursuit of segmentation reconstruct the vertical information of oblique distance；
S3, acquisition target elevation information, the three-dimensional point cloud result of display target building.
In the present embodiment step S1, as shown in Fig. 2 [S1,S2,…Sm-1,Sm,…SM] for sensor different time,
The two-dimensional SAR image that Different Flight position obtains to same target area.Sm-1SmBetween parallax range be b⊥m, SmTo s axles
Vertical range is δ (δ is caused because course line is unstable, can be eliminated by correcting during calculating).Vertical s (the azimuth-ranges of oblique distance
To the normal of place plane) parallel to s '.L is the vertical length of synthetic aperture of oblique distance.P1,PnIt is Target scalar on oblique distance is vertical
Two tested points, r0It is main picture centre oblique distance.Used as preferred, the two-dimensional SAR image data includes orbit altitude, weight
Visit cycle, ranges of incidence angles, azimuth resolution, range resolution ratio, single channel scene size, maximum acquisition length, also including light
Fast c, wavelength X, incidence angle vector InA, low coverage (Near range) vector, centre-to-centre spacing (Center Range) vector, long distance
(Far Range) vector, azimuth resolution, range resolution ratio, oblique distance R0, horizontal range H0=R0*cos (InA), baseline to
Amount.
Used as preferred, the step S1 is specifically included：By sensor in different time, Different Flight position to same
Target Acquisition two-dimensional SAR image data.
As shown in Fig. 2 the step S2 is specifically included：
S21, the two-dimensional SAR image data of multiple scenes is carried out into aperture synthetic along oblique distance is vertical, will a wherein width as
Main image, other images carry out registration with this image, after registration in main image each pixel along the vertical same scattering list of oblique distance
Unit constitutes an observation signal；
S22, each observation signal is reconstructed using the method for being segmented weak orthogonal matching pursuit, obtains target structures thing
Along the vertical information of oblique distance.
Used as preferred, the step S21 is further included：Determination respectively is intercepted the orientation and distance of target image
To beginning and end, the signal of scattering unit is constituted into an information for time series.
Used as preferred, the step S22 is specifically included：
S221, acquisition m flight path image expression formulas Sm；
S222, by SmExpression formula carries out discrete conversion, obtain observation vector for compressed sensing algorithm, calculation matrix and
Scattering sparse matrix；
S223, carry out the vertical signal reconstruct of oblique distance.
Used as preferred, the step S221 is specifically included：
The complex values S of any one pixel in main imagem, true three-dimension reflection case can be regarded as in x-r planes
Projection, i.e., reflected signal is along the vertical integration of oblique distance：
In formula, γ (s) is to scatter volume reflectivity along the vertical distribution function of oblique distance, and λ is wavelength, b⊥mIt is flight path m, m-1
Parallax range, ro is main image center oblique distance.
In actual conditions, often baseline profile is uneven or baseline number is few for the data of acquisition, using Fourier transform side
Method carries out three-dimensional imaging and cannot obtain preferable result.According to compressive sensing theory, if signal certain transform domain it is sparse or
It is compressible, it is possible to use less than nyquist sampling rate observation data to the signal reconstruction, and radar target can be by minority
Several scattering center descriptions, that is, meet the openness of target.Therefore compressed sensing can be applied to chromatography SAR three-dimensional imagings
In, the vertical signal of oblique distance is reconstructed, target structures are obtained in the vertical information of oblique distance, so as to extract the elevation of building
Information.The step S222 is specifically included, to SmFlight path image carry out discrete representation：
In formula, SnIt is n-th target, N is along the vertical scattering object number of oblique distance, m=1...M, M<N；Above formula is passed through
Matrix is represented：
Y=Φ γ+ξ
In formula, y=Sm, it is M dimension observed quantities, represent the observed quantity of M bar flight paths；ξ=[ξ1,ξ2,…,ξM]T, to be independently distributed
White Gaussian noise, Φ=[Ф1, Ф2…Фn…ФN], it is the calculation matrix of M × N, γ=[γ1,γ2…,γn,…,γN]T, it is N-dimensional scattering sparse matrix；Such as Fig. 3 and Fig. 4 institutes
Show, observation vector y, calculation matrix Φ, N-dimensional scattering sparse matrix γ are carried out being segmented weak orthogonal matching pursuit algorithm treatment, obtain
Oblique distance information after to reconstructReconstruction signal is can obtain for γ using sparse matrix, so as to extract the elevation information of atural object.
It is the one kind in OMP innovatory algorithms to be segmented weak orthogonal matching pursuit (SWOMP), and each iteration can select multiple atoms.Simultaneously
It reduces the requirement to calculation matrix in the selection atomic time, it is most important that this algorithm does not need output signal degree of rarefication K, phase
There is original advantage than other algorithms for needing known signal degree of rarefication K.
From compressive sensing theory, if signal is sparse or compressible in certain transform domain, it is possible to use less than how Kui
The observation data of this special sample rate are to the signal reconstruction.Due to the image-forming principle of SAR, the vertical signal of oblique distance of SAR image capturings
As sparse signal, therefore compressed sensing can be applied in chromatography SAR three-dimensional imagings, obtain Target scalar in oblique distance
Vertical information, so as to extract the elevation information of atural object.
Embodiment 2
Scheme provides the 14 scape two-dimensional SAR image datas in certain region in the present embodiment.14 scape target areas are read using ENVI
The data in domain, and the form that data are preserved into MATLAB can be read is used as preparing data.Can also be in a chosen area
Single building 14 scape image datas as prepare data, the region of selection needs to record its seat in SAR images
Cursor position, the two-dimensional SAR image in the scape Beijing hotel of scheme selection 14 is used as preparation data.
Three-dimensional point cloud generation (the TomoSAR three-dimensional point clouds imaging algorithm based on compressed sensing SWOMP) to target area
(1) read and loading data
Digital independent will be prepared and be loaded into MATLB.
(2) target area position is determined
Determine to be intercepted respectively the orientation and distance of target image to beginning and end position.Read with two-dimensional array
The 14 scape image datas that represent of form, then this 14 two-dimensional arrays are constituted into an arrays for three-dimensional.
(3) initiation parameter
Image data parameter (light velocity c, carrier frequency f, wavelength lambda, incidence angle vector InA, Near range vector,
Center Range vectors, Far Range vectors, azimuth resolution (azimuth resolution), range resolution ratio
(range resolution), oblique distance R0, horizontal range H0=R0*COS (InA), basic lineal vector baseline)
Elevation parameter：Elevation discreet value is set
(4) the vertical information of oblique distance is obtained
For the chromatography vertical acquisition of information of SAR three-dimensional imaging oblique distances mainly have fourier transform method, Power estimation algorithm,
Compressed sensing restructing algorithm.Compared with there is special advantage both other, scheme selection is based on compressed sensing to compressed sensing restructing algorithm
SWOMP the vertical information of oblique distance of Target scalar is reconstructed.
(5) elevation information is calculated
The oblique distance information of Target scalar is obtained using step (4), then the elevation information of target is extracted by further calculating.
According to SWOPM algorithm flow Fig. 3, the parameter of input is needed in experiment sensing matrix A, observation vector y, iterations S, threshold value
α.Wherein, S default values are that 10, α spans are 0<α≤1.
(6) drawing three-dimensional point cloud achievement
The Target scalar for being formed will be reconstructed to show, and output effect figure, as shown in Figure 4.
Finally, the present processes are only preferably embodiment, are not intended to limit the scope of the present invention.It is all
Within the spirit and principles in the present invention, any modification, equivalent substitution and improvements made etc. should be included in protection of the invention
Within the scope of.
Claims (8)
1. it is a kind of to chromatograph SAR three-D imaging methods, it is characterised in that to comprise the following steps：
S1, the two-dimensional SAR image for obtaining target structures thing are simultaneously pre-processed, and constitute observation data set；
S2, the compressed sensing algorithm based on the weak orthogonal matching pursuit of segmentation reconstruct the vertical information of oblique distance；
S3, acquisition target elevation information, the three-dimensional point cloud result of display target building.
2. chromatography SAR three-D imaging methods according to claim 1, it is characterised in that the step S1 is specifically included：Adopt
The multiple original SAR data of target of looking over so as to check of navigating of collection, and the pre- place of two-dimentional high-resolution imaging registration phase compensation is carried out to initial data
Reason, so as to the observation data set for obtaining to carry out chromatographing SAR imagings.
3. chromatography SAR three-D imaging methods according to claim 2, it is characterised in that the two-dimensional SAR image data bag
Include orbit altitude, revisiting period, ranges of incidence angles, center oblique distance, azimuth resolution, range resolution ratio, single channel scene size,
Maximum obtains length.
4. chromatography SAR three-D imaging methods according to claim 2, it is characterised in that the step S2 is specifically included：
S21, the two-dimensional SAR image data of multiple scenes is carried out into aperture synthetic along oblique distance is vertical, will a wherein width as main shadow
Picture, other images carry out registration with this image, after registration in main image each pixel along the vertical same scattering unit structure of oblique distance
Into an observation signal；
S22, each observation signal is reconstructed using the method for being segmented weak orthogonal matching pursuit, obtains target structures thing along tiltedly
Away from vertical information.
5. chromatography SAR three-D imaging methods according to claim 4, it is characterised in that the step S21 is further wrapped
Include：Determine to be intercepted respectively the orientation and distance of target image to beginning and end, by a signal structure for scattering unit
Into an information for time series.
6. chromatography SAR three-D imaging methods according to claim 4, it is characterised in that the step S22 is specifically included：
S221, acquisition m flight path image expression formulas Sm；
S222, by SmExpression formula carries out discrete conversion, obtains observation vector for compressed sensing algorithm, calculation matrix and scattering
Sparse matrix；
S223, carry out the vertical signal reconstruct of oblique distance.
7. chromatography SAR three-D imaging methods according to claim 6, it is characterised in that the step S221 is specifically included,
The complex values S of any one pixel in main imagem, as true three-dimension reflection case in the projection of x-r planes, that is, reflect letter
Number along the vertical integration of oblique distance：
In formula, γ (s) is to scatter volume reflectivity along the vertical distribution function of oblique distance, and λ is wavelength, b⊥mIt is the baseline of flight path m, m-1
Distance, ro is main image center oblique distance.
8. chromatography SAR three-D imaging methods according to claim 4, it is characterised in that the step S222 is specifically included：
To m flight path image expression formulas SmCarry out discrete representation：
In formula, SnIt is n-th target, N is along the vertical scattering object number of oblique distance, m=1...M, M<<N；Above formula is passed through into matrix
Represent：
Y=Φ γ+ξ
In formula, y=Sm, it is M dimension observed quantities, represent the observed quantity of M bar flight paths；
ξ=[ξ1,ξ2,…,ξM]T, it is the white Gaussian noise being independently distributed；
Φ=[Φ1,Φ2,…Φn,…ΦN], it is the calculation matrix of M × N；
γ=[γ1,γ2…,γn,…,γN]TIt is sparse matrix；
Observation vector y, calculation matrix Φ, sparse matrix γ are carried out to be segmented weak orthogonal matching pursuit algorithm treatment, is reconstructed
Oblique distance information afterwards
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108646244A (en) * | 2018-03-28 | 2018-10-12 | 中科卫星应用德清研究院 | Measure the analysis method and system of five dimension deformation of building |
| CN109212529A (en) * | 2018-11-02 | 2019-01-15 | 国网四川省电力公司电力应急中心 | A kind of electric power pylon monitoring method and device |
| CN109444886A (en) * | 2018-12-30 | 2019-03-08 | 成都汇蓉国科微系统技术有限公司 | A kind of parallel computation processing method, system and the medium of Bistatic SAR real time imagery |
| CN111679277A (en) * | 2020-05-28 | 2020-09-18 | 电子科技大学 | Multi-baseline chromatography SAR three-dimensional imaging method based on SBRIM algorithm |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20110175770A1 (en) * | 2009-06-30 | 2011-07-21 | Petros Boufounos | High Resolution SAR Imaging Using Non-Uniform Pulse Timing |
| CN102662171A (en) * | 2012-04-23 | 2012-09-12 | 电子科技大学 | Synthetic aperture radar (SAR) tomography three-dimensional imaging method |
| US8665132B2 (en) * | 2008-12-10 | 2014-03-04 | The United States Of America As Represented By The Secretary Of The Army | System and method for iterative fourier side lobe reduction |
| CN105388476A (en) * | 2015-12-28 | 2016-03-09 | 河南工业大学 | SAR imaging method based on joint sparsity model |
-
2016
- 2016-12-28 CN CN201611239581.XA patent/CN106872977A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US8665132B2 (en) * | 2008-12-10 | 2014-03-04 | The United States Of America As Represented By The Secretary Of The Army | System and method for iterative fourier side lobe reduction |
| US20110175770A1 (en) * | 2009-06-30 | 2011-07-21 | Petros Boufounos | High Resolution SAR Imaging Using Non-Uniform Pulse Timing |
| CN102662171A (en) * | 2012-04-23 | 2012-09-12 | 电子科技大学 | Synthetic aperture radar (SAR) tomography three-dimensional imaging method |
| CN105388476A (en) * | 2015-12-28 | 2016-03-09 | 河南工业大学 | SAR imaging method based on joint sparsity model |
Non-Patent Citations (2)
| Title |
|---|
| THOMAS BLUMENSATH等: "Stagewise Weak Gradient Pursuits", 《IEEE TRANSACTIONS ON SIGNAL PROCESSING》 * |
| 林珲等: "SAR层析成像的基本原理、关键技术和应用领域", 《测绘地理信息》 * |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108646244A (en) * | 2018-03-28 | 2018-10-12 | 中科卫星应用德清研究院 | Measure the analysis method and system of five dimension deformation of building |
| CN109212529A (en) * | 2018-11-02 | 2019-01-15 | 国网四川省电力公司电力应急中心 | A kind of electric power pylon monitoring method and device |
| CN109212529B (en) * | 2018-11-02 | 2021-06-22 | 国网四川省电力公司电力应急中心 | Method and device for monitoring power transmission tower |
| CN109444886A (en) * | 2018-12-30 | 2019-03-08 | 成都汇蓉国科微系统技术有限公司 | A kind of parallel computation processing method, system and the medium of Bistatic SAR real time imagery |
| CN111679277A (en) * | 2020-05-28 | 2020-09-18 | 电子科技大学 | Multi-baseline chromatography SAR three-dimensional imaging method based on SBRIM algorithm |
| CN111679277B (en) * | 2020-05-28 | 2022-05-03 | 电子科技大学 | Multi-baseline chromatography SAR three-dimensional imaging method based on SBRIM algorithm |
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